New Indices for Refining Multiple Choice Questions

Abstract EN

Multiple choice questions (MCQs) are one of the most popular tools to evaluate learning and knowledge in higher education.

Nowadays, there are a few indices to measure reliability and validity of these questions, for instance, to check the difficulty of a particular question (item) or the ability to discriminate from less to more knowledge.

In this work two new indices have been constructed: (i) the no answer index measures the relationship between the number of errors and the number of no answers; (ii) the homogeneity index measures homogeneity of the wrong responses (distractors).

The indices are based on the lack-of-fit statistic, whose distribution is approximated by a chi-square distribution for a large number of errors.

An algorithm combining several traditional and new indices has been developed to refine continuously a database of MCQs.

The final objective of this work is the classification of MCQs from a large database of items in order to produce an automated-supervised system of generating tests with specific characteristics, such as more or less difficulty or capacity of discriminating knowledge of the topic.